Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.
Purpose: Detection of breast cancer (BC) metastasis at the early stage is important for the assessment of BC progression status. Image analysis represents a valuable tool for the management of oncological patients. Our preliminary study combined imaging parameters from hybrid 18F-FDG-PET/MRI and the expression level of the transcriptional factor Yin Yang 1 (YY1) for the detection of early metastases. Methods: The study enrolled suspected n = 217 BC patients that underwent 18F-FDG-PET/MRI scans. The analysis retrospectively included n = 55 subjects. n = 40 were BC patients and n = 15 imaging-negative female individuals were healthy subjects (HS). Standard radiomics parameters were extracted from PET/MRI image. RNA was obtained from peripheral blood mononuclear cells and YY1 expression level was evaluated by real time reverse transcription polymerase chain reactions (qRT-PCR). An enzyme-linked immuosorbent assay (ELISA) was used to determine the amount of YY1 serum protein. Statistical comparison between subgroups was evaluated by Mann-Whitney U and Spearman’s tests. Results: Radiomics showed a significant positive correlation between Greg-level co-occurrence matrix (GLCM) and standardized uptake value maximum (SUVmax) (r = 0.8 and r = 0.8 respectively) in BC patients. YY1 level was significant overexpressed in estrogen receptor (ER)-positive/progesteron receptor-positive/human epidermal growth factor receptor2-negative (ER+/PR+/HER2-) subtype of BC patients with synchronous metastasis (SM) at primary diagnosis compared to metachronous metastasis (MM) and HS (p < 0.001) and correlating significantly with 18F-FDG-uptake parameter (SUVmax) (r = 0.48). Conclusions: The combination of functional 18F-FDG-PET/MRI parameters and molecular determination of YY1 could represent a novel integrated approach to predict synchronous metastatic disease with more accuracy than 18F-FDG-PET/MRI alone.
The lack of validation and standardization represents the main drawback for a clear role of whole-body diffusion weighted imaging (WB-DWI) for prediction and assessment of treatment response in Hodgkin’s lymphoma (HL). We explored the reliability of an automatic approach based on the WB-DWI technique for prediction and assessment of response to treatment in patients with HL. The study included 20 HL patients, who had whole-body positron emission tomography (PET)/ magnetic resonance Imaging (MRI) performed before, during and after chemotherapy. Using the syngo.via MR Total Tumor Load tool, we automatically extracted values of diffusion volume (DV) and its associated histogram features by WB-DWI images, and evaluated their utility in predicting and assessing interim and end-of-treatment (EOT) response. The Mann–Whitney test followed by receiver operator characteristic (ROC) analysis was performed between features and their inter-time point percentage differences for patients having a complete or partial treatment response, revealing that several WB-DWI associated features allowed for prediction of interim response and both prediction and assessment of EOT response. Our proposed method offers huge advantages in terms of saving time and work, enabling clinicians to draw conclusions relating to HL treatment response in a fully automatic way, and encloses, also, all DWI advantages compared to PET/ computed tomography (CT).
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